You Defined Your Metric Once. So Why Don’t Your AI Tools Agree?

Executive summary AI tools don’t agree because each one writes its own query, and the query is where a metric gets computed. A shared definition tells a tool what retention means, but the tool still decides which tables to join and which rows to count. In the mental_health database from BIRD-Interact, a public text-to-SQL benchmark, one join put 42 of 157 doctors’ rates wrong. It’s critical to compute each metric once, in a universal semantic layer so that people can use whichever AI tool they like and still get the same number. Compute each defined metric once, in a universal semantic layer (with the AI Computation Engine, ACE), and any AI tool your team picks returns the same number. Every model on the BIRD-Interact benchmark scored 72% to 137% higher on 410 business questions (best of three runs) with AtScale. Not every tool can read your definition, so a BI tool and an agent may each rebuild it their own way. Writing…